Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques

Autor: Wang, Zeheng, Li, Liang, Leon, Ross C. C., Yang, Jinlin, Shi, Junjie, van der Laan, Timothy, Usman, Muhammad

The semiconductors industry benefits greatly from the integration of Machine Learning (ML)-based techniques in Technology Computer-Aided Design (TCAD) methods. The performance of ML models however relies heavily on the quality and quantity of trainin

Externí odkaz: http://arxiv.org/abs/2105.11453

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Fundamental Aspects of Semiconductor Device Modeling Associated With Discrete Impurities: Monte Carlo Simulation Scheme

Autor: Takayuki Fukui, Nobuyuki Sano

Publikováno v: IEEE Transactions on Electron Devices. 68:5394-5399

We discuss the theoretical basis of the Boltzmann transport equation (BTE) and the Monte Carlo (MC) simulation scheme to take into account the discrete nature of impurities in semiconductor substrates. This is achieved by explicitly considering the s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::172a19b34ad4e9231dcb3bd013b443d2
https://doi.org/10.1109/ted.2021.3082804

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Machine Learning Algorithms for Semiconductor Device Modeling

Autor: Yogendra Gupta, Ashish Sharma, Niketa Sharma, Harish Kumar Sharma

Publikováno v: VLSI and Hardware Implementations Using Modern Machine Learning Methods ISBN: 9781003201038
VLSI and Hardware Implementations Using Modern Machine Learning Methods

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::54b1f277b5d37c537693fc5805a1d4b0
https://doi.org/10.1201/9781003201038-9